In [ ]:
from autoviz.AutoViz_Class import AutoViz_Class

%matplotlib inline

AV = AutoViz_Class()

viz = AV.AutoViz("vax_demog_nationality.csv", sep=',')
Shape of your Data Set loaded: (98118, 55)
#######################################################################################
######################## C L A S S I F Y I N G  V A R I A B L E S  ####################
#######################################################################################
Classifying variables in data set...
Data cleaning improvement suggestions. Complete them before proceeding to ML modeling.
  Nuniques dtype Nulls Nullpercent NuniquePercent Value counts Min Data cleaning improvement suggestions
full_malaysia 3855 int64 0 0.000000 3.928943 0
partial_malaysia 3845 int64 0 0.000000 3.918751 0
booster_malaysia 3021 int64 0 0.000000 3.078946 0
partial_indonesia 870 int64 0 0.000000 0.886687 0
full_indonesia 867 int64 0 0.000000 0.883630 0
partial_bangladesh 665 int64 0 0.000000 0.677755 0
full_bangladesh 657 int64 0 0.000000 0.669602 0
date 621 object 0 0.000000 0.632911 158
booster_indonesia 616 int64 0 0.000000 0.627815 0
booster_bangladesh 593 int64 0 0.000000 0.604374 0
full_philippines 524 int64 0 0.000000 0.534051 0
partial_philippines 505 int64 0 0.000000 0.514686 0
booster2_malaysia 497 int64 0 0.000000 0.506533 0
partial_myanmar 491 int64 0 0.000000 0.500418 0
full_myanmar 480 int64 0 0.000000 0.489207 0
full_nepal 403 int64 0 0.000000 0.410730 0
partial_nepal 402 int64 0 0.000000 0.409711 0
booster_myanmar 396 int64 0 0.000000 0.403596 0
partial_missing 393 int64 0 0.000000 0.400538 0
full_missing 377 int64 0 0.000000 0.384231 0
partial_other 336 int64 0 0.000000 0.342445 0
full_other 328 int64 0 0.000000 0.334291 0
partial_india 317 int64 0 0.000000 0.323080 0
booster_nepal 316 int64 0 0.000000 0.322061 0
full_india 311 int64 0 0.000000 0.316965 0
booster_philippines 254 int64 0 0.000000 0.258872 0
booster_missing 253 int64 0 0.000000 0.257853 0
booster_other 248 int64 0 0.000000 0.252757 0
booster_india 233 int64 0 0.000000 0.237469 0
partial_pakistan 224 int64 0 0.000000 0.228297 0
full_pakistan 217 int64 0 0.000000 0.221162 0
booster_pakistan 166 int64 0 0.000000 0.169184 0
full_china 164 int64 0 0.000000 0.167146 0
full_vietnam 162 int64 0 0.000000 0.165107 0
partial_china 160 int64 0 0.000000 0.163069 0
district 158 object 0 0.000000 0.161031 621
partial_vietnam 157 int64 0 0.000000 0.160011 0
partial_thailand 146 int64 0 0.000000 0.148800 0
full_thailand 142 int64 0 0.000000 0.144724 0
booster_china 119 int64 0 0.000000 0.121283 0
booster_vietnam 117 int64 0 0.000000 0.119244 0
booster_thailand 87 int64 0 0.000000 0.088669 0
booster2_other 53 int64 0 0.000000 0.054017 0
booster2_bangladesh 45 int64 0 0.000000 0.045863 0
booster2_myanmar 43 int64 0 0.000000 0.043825 0
booster2_missing 35 int64 0 0.000000 0.035671 0
booster2_indonesia 32 int64 0 0.000000 0.032614 0
booster2_philippines 30 int64 0 0.000000 0.030575 0
booster2_nepal 28 int64 0 0.000000 0.028537 0
booster2_china 26 int64 0 0.000000 0.026499 0
booster2_india 19 int64 0 0.000000 0.019364 0
state 16 object 0 0.000000 0.016307 621
booster2_vietnam 12 int64 0 0.000000 0.012230 0
booster2_thailand 12 int64 0 0.000000 0.012230 0
booster2_pakistan 9 int64 0 0.000000 0.009173 0
    55 Predictors classified...
        No variables removed since no ID or low-information variables found in data set
30 numeric variables in data exceeds limit, taking top 30 variables
    List of variables selected: ['partial_malaysia', 'full_malaysia', 'booster_malaysia', 'booster2_malaysia', 'partial_indonesia', 'full_indonesia', 'booster_indonesia', 'booster2_indonesia', 'partial_bangladesh', 'full_bangladesh', 'booster_bangladesh', 'booster2_bangladesh', 'partial_myanmar', 'full_myanmar', 'booster_myanmar', 'booster2_myanmar', 'partial_philippines', 'full_philippines', 'booster_philippines', 'booster2_philippines', 'partial_nepal', 'full_nepal', 'booster_nepal', 'booster2_nepal', 'partial_india', 'full_india', 'booster_india', 'booster2_india', 'partial_pakistan', 'full_pakistan']
   Total columns > 30, too numerous to print.
Number of All Scatter Plots = 465
Image size of 1500x87200 pixels is too large. It must be less than 2^16 in each direction.
Could not draw Pair Scatter Plots
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
File ~\AppData\Roaming\Python\Python310\site-packages\IPython\core\formatters.py:338, in BaseFormatter.__call__(self, obj)
    336     pass
    337 else:
--> 338     return printer(obj)
    339 # Finally look for special method names
    340 method = get_real_method(obj, self.print_method)

File ~\AppData\Roaming\Python\Python310\site-packages\IPython\core\pylabtools.py:152, in print_figure(fig, fmt, bbox_inches, base64, **kwargs)
    149     from matplotlib.backend_bases import FigureCanvasBase
    150     FigureCanvasBase(fig)
--> 152 fig.canvas.print_figure(bytes_io, **kw)
    153 data = bytes_io.getvalue()
    154 if fmt == 'svg':

File ~\AppData\Roaming\Python\Python310\site-packages\matplotlib\backend_bases.py:2308, in FigureCanvasBase.print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
   2301     bbox_inches = rcParams['savefig.bbox']
   2303 if (self.figure.get_layout_engine() is not None or
   2304         bbox_inches == "tight"):
   2305     # we need to trigger a draw before printing to make sure
   2306     # CL works.  "tight" also needs a draw to get the right
   2307     # locations:
-> 2308     renderer = _get_renderer(
   2309         self.figure,
   2310         functools.partial(
   2311             print_method, orientation=orientation)
   2312     )
   2313     with getattr(renderer, "_draw_disabled", nullcontext)():
   2314         self.figure.draw(renderer)

File ~\AppData\Roaming\Python\Python310\site-packages\matplotlib\backend_bases.py:1559, in _get_renderer(figure, print_method)
   1556     print_method = stack.enter_context(
   1557         figure.canvas._switch_canvas_and_return_print_method(fmt))
   1558 try:
-> 1559     print_method(io.BytesIO())
   1560 except Done as exc:
   1561     renderer, = exc.args

File ~\AppData\Roaming\Python\Python310\site-packages\matplotlib\backend_bases.py:2204, in FigureCanvasBase._switch_canvas_and_return_print_method.<locals>.<lambda>(*args, **kwargs)
   2200     optional_kws = {  # Passed by print_figure for other renderers.
   2201         "dpi", "facecolor", "edgecolor", "orientation",
   2202         "bbox_inches_restore"}
   2203     skip = optional_kws - {*inspect.signature(meth).parameters}
-> 2204     print_method = functools.wraps(meth)(lambda *args, **kwargs: meth(
   2205         *args, **{k: v for k, v in kwargs.items() if k not in skip}))
   2206 else:  # Let third-parties do as they see fit.
   2207     print_method = meth

File ~\AppData\Roaming\Python\Python310\site-packages\matplotlib\_api\deprecation.py:410, in delete_parameter.<locals>.wrapper(*inner_args, **inner_kwargs)
    400     deprecation_addendum = (
    401         f"If any parameter follows {name!r}, they should be passed as "
    402         f"keyword, not positionally.")
    403     warn_deprecated(
    404         since,
    405         name=repr(name),
   (...)
    408                  else deprecation_addendum,
    409         **kwargs)
--> 410 return func(*inner_args, **inner_kwargs)

File ~\AppData\Roaming\Python\Python310\site-packages\matplotlib\backends\backend_agg.py:517, in FigureCanvasAgg.print_png(self, filename_or_obj, metadata, pil_kwargs, *args)
    468 @_api.delete_parameter("3.5", "args")
    469 def print_png(self, filename_or_obj, *args,
    470               metadata=None, pil_kwargs=None):
    471     """
    472     Write the figure to a PNG file.
    473 
   (...)
    515         *metadata*, including the default 'Software' key.
    516     """
--> 517     self._print_pil(filename_or_obj, "png", pil_kwargs, metadata)

File ~\AppData\Roaming\Python\Python310\site-packages\matplotlib\backends\backend_agg.py:463, in FigureCanvasAgg._print_pil(self, filename_or_obj, fmt, pil_kwargs, metadata)
    458 def _print_pil(self, filename_or_obj, fmt, pil_kwargs, metadata=None):
    459     """
    460     Draw the canvas, then save it using `.image.imsave` (to which
    461     *pil_kwargs* and *metadata* are forwarded).
    462     """
--> 463     FigureCanvasAgg.draw(self)
    464     mpl.image.imsave(
    465         filename_or_obj, self.buffer_rgba(), format=fmt, origin="upper",
    466         dpi=self.figure.dpi, metadata=metadata, pil_kwargs=pil_kwargs)

File ~\AppData\Roaming\Python\Python310\site-packages\matplotlib\backends\backend_agg.py:399, in FigureCanvasAgg.draw(self)
    397 def draw(self):
    398     # docstring inherited
--> 399     self.renderer = self.get_renderer()
    400     self.renderer.clear()
    401     # Acquire a lock on the shared font cache.

File ~\AppData\Roaming\Python\Python310\site-packages\matplotlib\_api\deprecation.py:384, in delete_parameter.<locals>.wrapper(*inner_args, **inner_kwargs)
    379 @functools.wraps(func)
    380 def wrapper(*inner_args, **inner_kwargs):
    381     if len(inner_args) <= name_idx and name not in inner_kwargs:
    382         # Early return in the simple, non-deprecated case (much faster than
    383         # calling bind()).
--> 384         return func(*inner_args, **inner_kwargs)
    385     arguments = signature.bind(*inner_args, **inner_kwargs).arguments
    386     if is_varargs and arguments.get(name):

File ~\AppData\Roaming\Python\Python310\site-packages\matplotlib\backends\backend_agg.py:416, in FigureCanvasAgg.get_renderer(self, cleared)
    414 reuse_renderer = (self._lastKey == key)
    415 if not reuse_renderer:
--> 416     self.renderer = RendererAgg(w, h, self.figure.dpi)
    417     self._lastKey = key
    418 elif cleared:

File ~\AppData\Roaming\Python\Python310\site-packages\matplotlib\backends\backend_agg.py:84, in RendererAgg.__init__(self, width, height, dpi)
     82 self.width = width
     83 self.height = height
---> 84 self._renderer = _RendererAgg(int(width), int(height), dpi)
     85 self._filter_renderers = []
     87 self._update_methods()

ValueError: Image size of 1500x87200 pixels is too large. It must be less than 2^16 in each direction.
<Figure size 1500x87200 with 435 Axes>
[nltk_data] Downloading collection 'popular'
[nltk_data]    | 
[nltk_data]    | Downloading package cmudict to C:\Users\MuhammadimYus
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[nltk_data]    |   Unzipping corpora\cmudict.zip.
[nltk_data]    | Downloading package gazetteers to C:\Users\Muhammadim
[nltk_data]    |     Yusoff\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\gazetteers.zip.
[nltk_data]    | Downloading package genesis to C:\Users\MuhammadimYus
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[nltk_data]    |   Unzipping corpora\genesis.zip.
[nltk_data]    | Downloading package gutenberg to C:\Users\MuhammadimY
[nltk_data]    |     usoff\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\gutenberg.zip.
[nltk_data]    | Downloading package inaugural to C:\Users\MuhammadimY
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[nltk_data]    |   Unzipping corpora\inaugural.zip.
[nltk_data]    | Downloading package movie_reviews to C:\Users\Muhamma
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[nltk_data]    |   Unzipping corpora\movie_reviews.zip.
[nltk_data]    | Downloading package names to C:\Users\MuhammadimYusof
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[nltk_data]    |   Unzipping corpora\names.zip.
[nltk_data]    | Downloading package shakespeare to C:\Users\Muhammadi
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[nltk_data]    |   Unzipping corpora\shakespeare.zip.
[nltk_data]    | Downloading package stopwords to C:\Users\MuhammadimY
[nltk_data]    |     usoff\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\stopwords.zip.
[nltk_data]    | Downloading package treebank to C:\Users\MuhammadimYu
[nltk_data]    |     soff\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\treebank.zip.
[nltk_data]    | Downloading package twitter_samples to C:\Users\Muham
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[nltk_data]    |   Unzipping corpora\twitter_samples.zip.
[nltk_data]    | Downloading package omw to C:\Users\MuhammadimYusoff\
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[nltk_data]    | Downloading package wordnet to C:\Users\MuhammadimYus
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[nltk_data]    | Downloading package wordnet2021 to C:\Users\Muhammadi
[nltk_data]    |     mYusoff\AppData\Roaming\nltk_data...
[nltk_data]    | Downloading package wordnet31 to C:\Users\MuhammadimY
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[nltk_data]    | Downloading package wordnet_ic to C:\Users\Muhammadim
[nltk_data]    |     Yusoff\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping corpora\wordnet_ic.zip.
[nltk_data]    | Downloading package words to C:\Users\MuhammadimYusof
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[nltk_data]    |   Unzipping corpora\words.zip.
[nltk_data]    | Downloading package maxent_ne_chunker to C:\Users\Muh
[nltk_data]    |     ammadimYusoff\AppData\Roaming\nltk_data...
[nltk_data]    |   Unzipping chunkers\maxent_ne_chunker.zip.
[nltk_data]    | Downloading package punkt to C:\Users\MuhammadimYusof
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[nltk_data]    | Downloading package averaged_perceptron_tagger to C:\
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[nltk_data]    |   Unzipping taggers\averaged_perceptron_tagger.zip.
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[nltk_data]  Done downloading collection popular
Could not draw wordcloud plot for date
All Plots done
Time to run AutoViz = 1455 seconds 

 ###################### AUTO VISUALIZATION Completed ########################